Epilepsy is a severe neurological disorder that causes seizures. It is detected by analyzing the electrical impulses of the human brain. Monitoring the brain is commonly done using an electroencephalogram (EEG). Seizure detection from the large recorded EEG dataset is a demanding task. However, numerous machine learning classifiers and the appropriate features can detect seizures. The Hjorth and statistical parameters are used in this study to provide an effective method for field programmable gate array (FPGA) realization of epileptic seizure detectors from EEG signals. Mobility, interquartile range (IQR), median absolute deviation (MAD), energy, non-linear energy, and simple square integral (SSI) are the various features analyzed in this work. The hardware architecture of the seizure detection system is captured in Verilog hardware descriptive language and realized on the Artix-7 FPGA. This paper presents three separate seizure detection models designed by pairing three different features with the Quadratic discriminant analysis (QDA) classifier. Among these three models, the energy and non-linear energy-based seizure detection system offers better performance than existing seizure detection systems because it possesses the lowest dynamic power consumption (0.116 µW), the highest design accuracy (99.4 %), and the highest sensitivity (100 %), which makes it the best seizure detection system for real-time applications.

中文翻译：

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使用 QDA 分类器的癫痫发作检测系统的节能 FPGA 实现

癫痫是一种导致癫痫发作的严重神经系统疾病。它是通过分析人脑的电脉冲来检测的。通常使用脑电图（EEG）来监测大脑。从大量记录的脑电图数据集中检测癫痫发作是一项艰巨的任务。然而，许多机器学习分类器和适当的特征可以检测癫痫发作。本研究中使用 Hjorth 和统计参数，为根据 EEG 信号现场可编程门阵列 (FPGA) 实现癫痫发作探测器提供有效方法。迁移率、四分位距 (IQR)、中值绝对偏差 (MAD)、能量、非线性能量和简单平方积分 (SSI) 是本工作中分析的各种特征。癫痫检测系统的硬件架构采用 Verilog 硬件描述语言捕获，并在 Artix-7 FPGA 上实现。本文提出了三种独立的癫痫发作检测模型，通过将三个不同的特征与二次判别分析（QDA）分类器配对而设计。在这三种模型中，基于能量和非线性能量的癫痫检测系统比现有的癫痫检测系统具有更好的性能，因为它具有最低的动态功耗（0.116 µW）、最高的设计精度（99.4%）和最高的设计精度。灵敏度 (100 %)，这使其成为实时应用的最佳癫痫检测系统。